Natural Language Generation: Decoding Techniques and Training Challenges

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Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Concentration Inequalities

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Natural Language Generation

Explores natural language generation, focusing on building systems that produce coherent text for human consumption using various decoding methods and evaluation metrics.

Empirical Estimation: Distribution Estimation

Covers methods to estimate probability distributions from samples.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Diffusion Models in Generative Modeling

Explores diffusion models in generative modeling, covering probability estimation, data generation, and model evaluation.

Quantiles, Sampling, Histogram Density

Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.